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10.01.2022 | Original Article

Urinary metabolomics to develop predictors for pediatric acute kidney injury

verfasst von: Alexandra Franiek, Atul Sharma, Vedran Cockovski, David S. Wishart, Michael Zappitelli, Tom D. Blydt-Hansen

Erschienen in: Pediatric Nephrology | Ausgabe 9/2022

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Abstract

Background

Acute kidney injury (AKI) is characterized by an abrupt decline in glomerular filtration rate (GFR). We sought to identify separate early urinary metabolomic signatures at AKI onset (with-AKI) and prior to onset of functional impairment (pre-AKI).

Methods

Pre-AKI (n=15), AKI (n=22), and respective controls (n=30) from two prospective PICU cohort studies provided urine samples which were analyzed by GC-MS and DI-MS mass spectrometry (193 metabolites). The cohort (n=58) was 8.7±6.4 years old and 66% male. AKI patients had longer PICU stays, higher PRISM scores, vasopressors requirement, and respiratory diagnosis and less commonly had trauma or post-operative diagnosis. Urine was collected within 2–3 days after admission and daily until day 5 or 14.

Results

The metabolite classifiers for pre-AKI samples (1.5±1.1 days prior to AKI onset) had a cross-validated area under receiver operator curve (AUC)=0.93 (95%CI 0.85–1.0); with-AKI samples had an AUC=0.94 (95%CI 0.87–1.0). A parsimonious pre-AKI classifier with 13 metabolites was similarly robust (AUC=0.96, 95%CI 0.89–1.0). Both classifiers were similar and showed modest correlation of high-ranking metabolites (tau=0.47, p<0.001).

Conclusions

This exploratory study demonstrates the potential of a urine metabolite classifier to detect AKI-risk in pediatric populations earlier than the current standard of diagnosis with the need for external validation.

Graphical abstract

A higher resolution version of the Graphical abstract is available as Supplementary information with inner reference to ESM for GA

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Titel: Urinary metabolomics to develop predictors for pediatric acute kidney injury
verfasst von: Alexandra Franiek
Atul Sharma
Vedran Cockovski
David S. Wishart
Michael Zappitelli
Tom D. Blydt-Hansen
Publikationsdatum: 10.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Pediatric Nephrology / Ausgabe 9/2022
Print ISSN: 0931-041X
Elektronische ISSN: 1432-198X
DOI: https://doi.org/10.1007/s00467-021-05380-6

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Urinary metabolomics to develop predictors for pediatric acute kidney injury